基于离散小波变换和Kalman滤波的直升机主减智能状态预测

PDF(1314 KB)

振动与冲击 ›› 2012, Vol. 31 ›› Issue (17) : 159-164.

论文

基于离散小波变换和Kalman滤波的直升机主减智能状态预测

刘立生杨宇航

作者信息 +

Intelligent condition prognostics for helicopter main gearboxusing discrete wavelet and Kalman filter

LIU Li-Sheng , YANG Yu-hang

Author information +

文章历史 +

摘要

主减速器（简称“主减”）是直升机传动系统的关键部件，它常处于高转速高负荷的恶劣环境下，对其运行状态进行预测，于直升机的安全性来说至关重要。鉴于此，提出了一种离散小波变换（DWT）、Kalman滤波以及Elman神经网络相结合的直升机主减智能状态预测系统：DWT使用“db44”母小波对振动信号进行分解提取特征向量，Kalman滤波对未来各时刻的特征向量进行预测，Elman神经网络对预测值进行故障辨识和分类。在Kalman滤波算法中，提出了一种新的预测算法，并用实验对该算法组成的系统进行验证，结果表明：该 Kalman滤波算法预测效果好，更适用于对主减的特征向量进行预测；离散小波变换（DWT）、Kalman滤波以及Elman神经网络相结合组成的智能状态预测系统是可行的，它能很好地对主减的未来状态进行预测。

Abstract

Main gearbox (MGB) is a key component of helicopter transmission system, it often runs in the atrocious condition of high revolving speed and high burthen, it is very important to achieve condition prognostics for the safe of helicopter. This paper puts forward a intelligent condition prognostic system for helicopter MGB using discrete wavelet transform (DWT), Kalman filter and Elman neural network. The mother wavelet of Daubechies 44 (db44) is selected to extract feature vectors in the process of DWT. Kalman filter is used for feature vectors prognostics, and Elman neural network is taken for fault distinguish and classification. In the algorithm of Kalman filter, a new prognostic method is proposed, and it is verified in the system by experiment. The results indicate that the prognostic outcome of the Kalman filter with this method is better, which is more applicable for the prognostics of feature vectors, and the intelligent condition prognostic system composed of DWT, Kalman filter and Elman neural network is feasible, which can predict the future condition of MGB accurately.

导出引用

刘立生杨宇航. 基于离散小波变换和Kalman滤波的直升机主减智能状态预测[J]. 振动与冲击, 2012, 31(17): 159-164

LIU Li-Sheng;YANG Yu-hang. Intelligent condition prognostics for helicopter main gearboxusing discrete wavelet and Kalman filter[J]. Journal of Vibration and Shock, 2012, 31(17): 159-164